Augmented Intelligence System Architecture

ABSTRACT

A system, method, and computer-readable medium are disclosed for cognitive information processing. The cognitive information processing includes receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system and an information processing system providing a cognitive computing function; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for performing cognitive inference and learningoperations.

Description of the Related Art

In general, “big data” refers to a collection of datasets so large andcomplex that they become difficult to process using typical databasemanagement tools and traditional data processing approaches. Thesedatasets can originate from a wide variety of sources, includingcomputer systems, mobile devices, credit card transactions, televisionbroadcasts, and medical equipment, as well as infrastructures associatedwith cities, sensor-equipped buildings and factories, and transportationsystems. Challenges commonly associated with big data, which may be acombination of structured, unstructured, and semi-structured data,include its capture, curation, storage, search, sharing, analysis andvisualization. In combination, these challenges make it difficult toefficiently process large quantities of data within tolerable timeintervals.

Nonetheless, big data analytics hold the promise of extracting insightsby uncovering difficult-to-discover patterns and connections, as well asproviding assistance in making complex decisions by analyzing differentand potentially conflicting options. As such, individuals andorganizations alike can be provided new opportunities to innovate,compete, and capture value.

One aspect of big data is “dark data,” which generally refers to datathat is either not collected, neglected, or underutilized. Examples ofdata that is not currently being collected includes location data priorto the emergence of companies such as Foursquare or social data prior tothe advent of companies such as Facebook. An example of data that isbeing collected, but may be difficult to access at the right time andplace, includes the side effects of certain spider bites while anaffected individual is on a camping trip. As another example, data thatis collected and available, but has not yet been productized or fullyutilized, may include disease insights from population-wide healthcarerecords and social media feeds. As a result, a case can be made thatdark data may in fact be of higher value than big data in general,especially as it can likely provide actionable insights when it iscombined with readily-available data.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed for performingcognitive inference and learning operations.

In one embodiment, the invention relates to a method for cognitiveinformation processing, comprising: receiving data from a plurality ofdata sources; processing the data from the plurality of data sources toprovide cognitively processed insights via an augmented intelligencesystem, the augmented intelligence system executing on a hardwareprocessor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function; performing a learning operation toiteratively improve the cognitively processed insights over time; and,providing the cognitively processed insights to a destination, thedestination comprising a cognitive application, the cognitiveapplication enabling a user to interact with the cognitive insights.

In another embodiment, the invention relates to a system comprising: ahardware processor; a data bus coupled to the hardware processor; and anon-transitory, computer-readable storage medium embodying computerprogram code, the non-transitory, computer-readable storage medium beingcoupled to the data bus, the computer program code interacting with aplurality of computer operations and comprising instructions executableby the hardware processor and configured for: receiving data from aplurality of data sources; processing the data from the plurality ofdata sources to provide cognitively processed insights via an augmentedintelligence system, the augmented intelligence system executing on ahardware processor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function; performing a learning operation toiteratively improve the cognitively processed insights over time; and,providing the cognitively processed insights to a destination, thedestination comprising a cognitive application, the cognitiveapplication enabling a user to interact with the cognitive insights.

In another embodiment, the invention relates to a non-transitory,computer-readable storage medium embodying computer program code, thecomputer program code comprising computer executable instructionsconfigured for: receiving data from a plurality of data sources;processing the data from the plurality of data sources to providecognitively processed insights via an augmented intelligence system, theaugmented intelligence system executing on a hardware processor of aninformation processing system, the augmented intelligence system and theinformation processing system providing a cognitive computing function;performing a learning operation to iteratively improve the cognitivelyprocessed insights over time; and, providing the cognitively processedinsights to a destination, the destination comprising a cognitiveapplication, the cognitive application enabling a user to interact withthe cognitive insights.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of an augmented intelligence system(AIS);

FIG. 3 is a simplified block diagram of an AIS reference model;

FIG. 4 is a simplified block diagram of an AIS platform;

FIG. 5 shows a simplified block diagram of components associated with acognitive process foundation;

FIG. 6 is a simplified block diagram of a plurality of AIS platformsimplemented within a hybrid cloud environment;

FIG. 7 shows components of a plurality of AIS platforms implementedwithin a hosted/private/hybrid cloud environment;

FIGS. 8a and 8b are a simplified process diagram showing the performanceof cognitive process promotion operations;

FIG. 9 is a simplified process diagram showing phases of a cognitiveprocess lifecycle;

FIGS. 10a through 10f show operations performed in a cognitive processlifecycle;

Figures Ila and 1 lb are a simplified process flow showing the lifecycleof cognitive agents implemented to perform AIS operations; and

FIG. 12 is a simplified block diagram of an AIS used to performpattern-based continuous learning operations.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for augmentedintelligence operations. Certain aspects of the invention reflect anappreciation that augmented intelligence is not technically differentfrom what is generally regarded as general artificial intelligence (AI).However, certain aspects of the invention likewise reflect anappreciation that typical implementations of augmented intelligence aremore oriented towards complementing, or reinforcing, the role humanintelligence plays when discovering relationships and solving problems.Likewise, various aspects of the invention reflect an appreciation thatcertain advances in general AI approaches may provide differentperspectives on how computers and software can participate in tasks thathave previously been thought of being exclusive to humans.

Certain aspects of the invention reflect an appreciation that processesand applications employing AI models have become common in recent years.However, certain aspects of the invention likewise reflect anappreciation that known approaches to building, deploying, andmaintaining such processes, applications and models at significant scalecan be challenging. More particularly, various technical hurdles canprevent operational success in AI application development anddeployment. As an example, empowering development teams to more easilydevelop AI systems and manage their end-to-end lifecycle can provechallenging.

Accordingly, certain aspects of the invention may reflect anappreciation that the ability to orchestrate a pipeline of AI componentsnot only facilitates chained deployment of an AI system, but will likelyreduce implementation intervals while simultaneously optimizing the useof human and computing resources. Likewise, certain aspects of theinvention reflect an appreciation that achieving consistency across AIimplementations may be facilitated by easily sharing machine learning(ML) models within the context of a standardized application modelingand execution language. In particular, such an approach may beadvantageous when it is agnostic to common application developmentplatforms and database conventions.

Certain aspects of the invention likewise reflect an appreciation thatthe development of ML models is often a small, but important part of theAI development process. However, getting ML models developed by datascientists and then putting them into production requires time andresources, both of which may be limited. Likewise, certain aspects ofthe invention reflect that AI systems are generally complex.Accordingly, a repeatable approach that reduces the skill required todevelop and deploy AI systems can assist in achieving scalability of AIinitiatives.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device. The computer readable storage medium maybe, for example, but is not limited to, an electronic storage device, amagnetic storage device, an optical storage device, an electromagneticstorage device, a semiconductor storage device, or any suitablecombination of the foregoing.

A non-exhaustive list of more specific examples of the computer readablestorage medium includes the following: a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), a staticrandom access memory (SRAM), a portable compact disc read-only memory(CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk,a mechanically encoded device such as punch-cards or raised structuresin a groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 is a generalized illustration of an information processing system100 that can be used to implement the system and method of the presentinvention. The information processing system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, a touchpad or touchscreen,and associated controllers, a hard drive or disk storage 106, andvarious other subsystems 108. In certain embodiments, the informationprocessing system 100 may also include a network port 110 operable toconnect to a network 140, which is likewise accessible by a serviceprovider server 142. The information processing system 100 likewiseincludes system memory 112, which is interconnected to the foregoing viaone or more buses 114. System memory 112 further comprises operatingsystem (OS) 116 and in certain embodiments may also comprise anaugmented intelligence system (AIS) 118. In these and other embodiments,the AIS 118 may likewise comprise a cognitive agent composition platform120 and a cognitive process orchestration platform 124. In certainembodiments, the cognitive agent composition platform 120 may include acognitive skill composition platform 122. In certain embodiments, theinformation processing system 100 may be implemented to download the AIS118 from the service provider server 142. In another embodiment, thefunctionality of the AIS 118 may be provided as a service from theservice provider server 142.

In certain embodiments, the AIS 118 may be implemented to performvarious cognitive computing operations. As used herein, cognitivecomputing broadly refers to a class of computing involving self-learningsystems that use techniques such as spatial navigation, machine vision,and pattern recognition to increasingly mimic the way the human brainworks. To be more specific, earlier approaches to computing typicallysolved problems by executing a set of instructions codified withinsoftware. In contrast, cognitive computing approaches are data-driven,sense-interpretation, insight-extracting, problem-solving,recommendation-making systems that have more in common with thestructure of the human brain than with the architecture of contemporary,instruction-driven computers.

To further differentiate these distinctions, traditional computers mustfirst be programmed by humans to perform specific tasks, while cognitivecomputing systems learn from their interactions with data and humansalike, and in a sense, program themselves to perform new tasks. Tosummarize the difference between the two, traditional computers aredesigned to calculate rapidly. In contrast, cognitive computing systemsare designed to quickly draw inferences from data and gain newknowledge.

Cognitive computing systems achieve these abilities by combining variousaspects of artificial intelligence, natural language processing, dynamiclearning, and hypothesis generation to render vast quantities ofintelligible data to assist humans in making better decisions. As such,cognitive computing systems can be characterized as having the abilityto interact naturally with people to extend what either humans, ormachines, could do on their own. Furthermore, they are typically able toprocess natural language, multi-structured data, and experience much inthe same way as humans. Moreover, they are also typically able to learna knowledge domain based upon the best available data and get better,and more immersive, over time.

It will be appreciated that more data is currently being produced everyday than was recently produced by human beings from the beginning ofrecorded time. Deep within this ever-growing mass of data is a class ofdata known as “dark data,” which includes neglected information, ambientsignals, and insights that can assist organizations and individuals inaugmenting their intelligence and deliver actionable insights throughthe implementation of cognitive processes.

As used herein, a cognitive process broadly refers to an instantiationof one or more associated cognitive computing operations, described ingreater detail herein. In certain embodiments, a cognitive process maybe implemented as a cloud-based, big data interpretive process thatlearns from user engagement and data interactions. In certainembodiments, such cognitive processes may be implemented to extractpatterns and insights from dark data sources that are currently almostcompletely opaque. Examples of dark data include disease insights frompopulation-wide healthcare records and social media feeds, or from newsources of information, such as sensors monitoring pollution in delicatemarine environments.

In certain embodiments, a cognitive process may be implemented toinclude a cognitive application. As used herein, a cognitive applicationbroadly refers to a software application that incorporates one or morecognitive processes. In certain embodiments, a cognitive application maybe implemented to incorporate one or more cognitive processes with otherfunctionalities, as described in greater detail herein.

Over time, it is anticipated that cognitive processes and applicationswill fundamentally change the ways in which many organizations operateas they invert current issues associated with data volume and variety toenable a smart, interactive data supply chain. Ultimately, cognitiveprocesses and applications hold the promise of receiving a user queryand immediately providing a data-driven answer from a masked data supplychain in response. As they evolve, it is likewise anticipated thatcognitive processes and applications may enable a new class of “sixthsense” processes and applications that intelligently detect and learnfrom relevant data and events to offer insights, predictions and advicerather than wait for commands. Just as web and mobile applications havechanged the way people access data, cognitive processes and applicationsmay change the way people consume, and become empowered by,multi-structured data such as emails, social media feeds, doctors notes,transaction records, and call logs.

However, the evolution of such cognitive processes and applications hasassociated challenges, such as how to detect events, ideas, images, andother content that may be of interest. For example, assuming that therole and preferences of a given user are known, how is the most relevantinformation discovered, prioritized, and summarized from large streamsof multi-structured data such as news feeds, blogs, social media,structured data, and various knowledge bases? To further the example,what can a healthcare executive be told about their competitor's marketshare? Other challenges include the creation of acontextually-appropriate visual summary of responses to questions orqueries.

FIG. 2 is a simplified block diagram of an augmented intelligence system(AIS) implemented in accordance with an embodiment of the invention. Asused herein, augmented intelligence broadly refers to an alternativeconceptualization of general artificial intelligence (AI) oriented tothe use of AI in an assistive role, with an emphasis on theimplementation of cognitive computing, described in greater detailherein, to enhance human intelligence rather than replace it. In certainembodiments, an AIS 118 may be implemented to include a cognitive agentcomposition platform 120 and a cognitive process orchestration platform124.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to compose cognitive agents 250, which are in turnorchestrated by the cognitive process orchestration platform 124 togenerate one or more cognitive insights 262, likewise described ingreater detail herein. As used herein, a cognitive agent 250 broadlyrefers to a computer program that performs a task with minimum specificdirections from users and learns from each interaction with data andhuman users. In certain embodiments, one or more cognitive agents 250may be implemented as deployable modules that aggregate the logic, dataand models required to implement an augmented intelligence operation.

In certain embodiments, a particular cognitive agent 250 may beimplemented to be triggered by other cognitive agents 250, timers, or byexternal requests. In certain embodiments, a cognitive agent 250 may becomposed from other cognitive agents 250 to create new functionalities.In certain embodiments, a cognitive agent 250 may be implemented toexpose its functionality through a web service, which can be used tointegrate it into a cognitive process or application, described ingreater detail herein. In certain embodiments, cognitive agents 250 maybe implemented to ingest various data, such as public 202, proprietary204, transaction, social 208, device 210, and ambient 212 data, toprovide a cognitive insight 262 or make a recommendation.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to use cognitive skills 226, input/output services,datasets, and data flows, or a combination thereof, to compose acognitive agent 250. In certain embodiments, a cognitive agent 250 maybe implemented with an integration layer. In certain embodiments, theintegration layer may be implemented to provide data to a particularcognitive agent 250 from a various data sources, services, such as a webservice, other cognitive agents 250, or a combination thereof. Incertain embodiments, the integration layer may be implemented to providea user interface (UI) to a cognitive agent 250. In certain embodiments,the UI may include a web interface, a mobile device interface, orstationary device interface.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to include a cognitive skill composition platform 122. Incertain embodiments, the cognitive skill composition platform 122 may beimplemented to compose a cognitive skill 226. As used herein, acognitive skill 226 broadly refers to the smallest distinct unit offunctionality in a cognitive agent 250 that can be invoked by one ormore inputs to produce one or more outputs.

In certain embodiments, a cognitive skill 226 may be implemented toexecute an atomic unit of work, which can be triggered by one or moreinputs to produce one or more outputs. In certain embodiments, theinputs and outputs may include services, managed content, databaseconnections, and so forth. In certain embodiments, cognitive skills 226may be implemented to be connected via input/output units, or synapses,which control the flow of data through an associated cognitive agent250.

In certain embodiments, one or more cognitive skills 226 may beimplemented to provide various disjointed functionalities in a cognitiveagent 250. In certain embodiments, such functionalities may includeingesting, enriching, and storing data from a data stream, training andtesting a machine learning (ML) algorithm to generate an ML model, andloading data from an external source, such as a file. In certainembodiments, such functionalities may likewise include transforming theraw data into a dataset for further processing, extracting features froma dataset, or invoking various services, such as web services familiarto those of skill in the art.

As used herein, a cognitive model 222 broadly refers to a machinelearning model that serves as a mathematical representation of areal-world process that can be facilitated by a cognitive computingoperation. In certain embodiments, the cognitive skill compositionplatform 122 may be implemented to compose a cognitive skill 226 fromone or more cognitive models 222. In certain embodiments, theimplementation of a cognitive model 222 may involve the implementationof two cognitive actions 224. In certain embodiments, the firstcognitive action 224 may be implemented to train the cognitive model 222and the second cognitive action 224 may be implemented to makepredictions based upon a set of unlabeled data to provide a cognitiveinsight 262.

In certain embodiments, a cognitive action 224 may be implemented as afunction, a batch job, or a daemon, all of which will be familiar toskilled practitioners of the art. In certain embodiments, cognitiveactions 224 may be implemented to be decoupled from a particularcognitive skill 226 such that they may be reused by other cognitiveskills 226. In various embodiments, a cognitive action 224 implementedas a batch job may be configured to run at certain intervals or betriggered to run when a certain event takes place.

In certain embodiments, a cognitive skill 226 may be implemented toinclude a definition identifying various dataset input requirements,cognitive insight 262 outputs, and datasets needed to complete thecognitive skill's 226 associated cognitive actions 224. In certainembodiments, an output of one cognitive skill 226 may be used as theinput to another cognitive skill 226 to build complex cognitive agents250. In various embodiments, certain cognitive skills 226 may beimplemented to control the flow of data through an associated cognitiveagent 250. In various embodiments, a cognitive skill 226 may beimplemented as a modular entity to interface a particular cognitiveagent 250 to certain external applications and Application ProgramInterfaces (APIs). In certain embodiments, a cognitive skill 226 may beimplemented to perform extract, transform, load (ETL) operations uponthe output of another cognitive skill 226, thereby serving as a wrapperfor an ML classifier or regressor.

As used herein, an input/output service broadly refers to a live linkthat is implemented to send and receive data. In certain embodiments,input/output services may be defined in input/output pairs that requireand deliver a payload to and from a cognitive agent 250. In certainembodiments, public 202, proprietary 204, transaction, social 208,device 210, and ambient 212 data may be ingested and processed by theAIS 118 to generate one or more datasets. As used herein, a datasetbroadly refers to a type of data input a cognitive agent 250 may beimplemented to ingest. In certain embodiments, such a dataset may beimplemented to include a definition that includes the source of the dataand its corresponding schema.

Various embodiments of the invention reflect an appreciation that theimplementation of cognitive skills 226 in certain embodiments maystreamline, or otherwise facilitate, the construction of certaincognitive agents 250. In various embodiments, certain cognitive skills226 may be implemented as micro services and published in a repositoryof AIS components, described in greater detail herein, as ready-to-useunits, which can be mixed and matched between cognitive computingprojects. Certain embodiments of the invention reflect an appreciationthat the ability to adopt an assembly model that supports the mixing andmatching of cognitive skills 226 between cognitive computing projectsmay minimize the effort required to rewrite code for new cognitiveagents 250, and by extension, shorten development cycles.

As shown in FIG. 2, examples of cognitive skills 226 used by thecognitive agent composition platform 120 to generate cognitive agents250 include semantic analysis 228, goal optimization 230, collaborativefiltering 232, and common sense reasoning 234. Other examples of suchcognitive skills 226 include natural language processing 236,summarization 238, temporal/spatial reasoning 240, and entity resolution242. As used herein, semantic analysis broadly refers to performingvarious analysis operations to achieve a semantic level of understandingabout language by relating syntactic structures.

In certain embodiments, various syntactic structures may be related fromthe levels of phrases, clauses, sentences, and paragraphs to the levelof the body of content as a whole, and to its language-independentmeaning. In certain embodiments, the semantic analysis 228 cognitiveskill may include processing a target sentence to parse it into itsindividual parts of speech, tag sentence elements that are related tocertain items of interest, identify dependencies between individualwords, and perform co-reference resolution. For example, if a sentencestates that the author really enjoys the hamburgers served by aparticular restaurant, then the name of the “particular restaurant” isco-referenced to “hamburgers.”

As likewise used herein, goal optimization broadly refers to performingmulti-criteria decision making operations to achieve a given goal ortarget objective. In certain embodiments, one or more goal optimization230 cognitive skills may be orchestrated by the cognitive compositionplatform 122 to generate a cognitive agent 250 for definingpredetermined goals, which in turn contribute to the generation of anassociated cognitive insight 262. For example, goals for planning avacation trip may include low cost (e.g., transportation andaccommodations), location (e.g., by the beach), and speed (e.g., shorttravel time). In this example, it will be appreciated that certain goalsmay be in conflict with another. As a result, a cognitive insight 262provided by the AIS 118 to a traveler may indicate that hotelaccommodations by a beach may cost more than they care to spend.

Collaborative filtering, as used herein, broadly refers to the processof filtering for information or patterns through the collaborativeinvolvement of multiple cognitive agents, viewpoints, data sources, andso forth. In certain embodiments, the application of such collaborativefiltering 232 cognitive skills may involve very large and differentkinds of data sets, including sensing and monitoring data, financialdata, and user data of various kinds. In certain embodiments,collaborative filtering may also refer to the process of makingautomatic predictions associated with predetermined interests of a userby collecting preferences or other information from many users. Forexample, if person ‘A’ has the same opinion as a person ‘B’ for a givenissue ‘x’, then an assertion can be made that person ‘A’ is more likelyto have the same opinion as person ‘B’ opinion on a different issue ‘y’than to have the same opinion on issue ‘y’ as a randomly chosen person.In certain embodiments, the collaborative filtering 206 cognitive skillmay be implemented with various recommendation engines familiar to thoseof skill in the art to make recommendations.

As used herein, common sense reasoning broadly refers to simulating thehuman ability to make deductions from common facts they inherently know.Such deductions may be made from inherent knowledge about the physicalproperties, purpose, intentions and possible behavior of ordinarythings, such as people, animals, objects, devices, and so on. In variousembodiments, certain common sense reasoning 234 cognitive skills may becomposed by the cognitive agent composition platform 120 to generate acognitive agent 250 that assists the AIS 118 in understanding anddisambiguating words within a predetermined context. In certainembodiments, the common sense reasoning 234 cognitive skill may be usedby the cognitive agent composition platform 120 to generate a cognitiveagent 250 that allows the AIS 118 to generate text or phrases related toa target word or phrase to perform deeper searches for the same terms.It will be appreciated that if the context of a word is betterunderstood, then a common sense understanding of the word can then beused to assist in finding better or more accurate information. Incertain embodiments, the better or more accurate understanding of thecontext of a word, and its related information, allows the AILS 118 tomake more accurate deductions, which are in turn used to generatecognitive insights 262.

As likewise used herein, natural language processing (NLP) broadlyrefers to interactions with a system, such as the AIS 118, through theuse of human, or natural, languages. In certain embodiments, various NLP210 cognitive skills may be implemented by the AIS 118 to achievenatural language understanding, which enables it to not only derivemeaning from human or natural language input, but to also generatenatural language output.

Summarization, as used herein, broadly refers to processing a set ofinformation, organizing and ranking it, and then generating acorresponding summary. As an example, a news article may be processed toidentify its primary topic and associated observations, which are thenextracted, ranked, and presented to the user. As another example, pageranking operations may be performed on the same news article to identifyindividual sentences, rank them, order them, and determine which of thesentences are most impactful in describing the article and its content.As yet another example, a structured data record, such as a patient'selectronic medical record (EMR), may be processed using certainsummarization 238 cognitive skills to generate sentences and phrasesthat describes the content of the EMR. In certain embodiments, varioussummarization 238 cognitive skills may be used by the cognitive agentcomposition platform 120 to generate to generate a cognitive agent 250that provides summarizations of content streams, which are in turn usedby the AIS 118 to generate cognitive insights 262.

As used herein, temporal/spatial reasoning broadly refers to reasoningbased upon qualitative abstractions of temporal and spatial aspects ofcommon sense knowledge, described in greater detail herein. For example,it is not uncommon for a particular set of data to change over time.Likewise, other attributes, such as its associated metadata, may alsochange over time. As a result, these changes may affect the context ofthe data. To further the example, the context of asking someone whatthey believe they should be doing at 3:00 in the afternoon during theworkday while they are at work may be quite different than asking thesame user the same question at 3:00 on a Sunday afternoon when they areat home. In certain embodiments, various temporal/spatial reasoning 214cognitive skills may be used by the cognitive agent composition platform120 to generate a cognitive agent 250 for determining the context ofqueries, and associated data, which are in turn used by the AIS 118 togenerate cognitive insights 262.

As likewise used herein, entity resolution broadly refers to the processof finding elements in a set of data that refer to the same entityacross different data sources (e.g., structured, non-structured,streams, devices, etc.), where the target entity does not share a commonidentifier. In certain embodiments, various entity resolution 216cognitive skills may be used by the cognitive agent composition platform120 to generate a cognitive agent 250 that can be used to identifysignificant nouns, adjectives, phrases or sentence elements thatrepresent various predetermined entities within one or more domains.From the foregoing, it will be appreciated that the generation of one ormore of the semantic analysis 228, goal optimization 230, collaborativefiltering 232, common sense reasoning 234, natural language processing236, summarization 238, temporal/spatial reasoning 240, and entityresolution 240 cognitive skills by the cognitive process orchestrationplatform 124 can facilitate the generation of a semantic, cognitivemodel.

In certain embodiments, the AIS 118 may receive public 202, proprietary204, transaction, social 208, device 210, and ambient 212 data, or acombination thereof, which is then processed by the AIS 118 to generateone or more cognitive graphs 230. As used herein, public 202 databroadly refers to any data that is generally available for consumptionby an entity, whether provided for free or at a cost. As likewise usedherein, proprietary 204 data broadly refers to data that is owned,controlled, or a combination thereof, by an individual user, group, ororganization, which is deemed important enough that it gives competitiveadvantage to that individual or organization. In certain embodiments,the organization may be a governmental, non-profit, academic or socialentity, a manufacturer, a wholesaler, a retailer, a service provider, anoperator of an AIS 118, and others. In certain embodiments, the publicdata 202 and proprietary 204 data may include structured,semi-structured, or unstructured data.

As used herein, transaction 206 data broadly refers to data describingan event, and is usually described with verbs. In typical usage,transaction data includes a time dimension, a numerical value, andcertain reference data, such as references to one or more objects. Incertain embodiments, the transaction 206 data may include credit ordebit card transaction data, financial services data of all kinds (e.g.,mortgages, insurance policies, stock transfers, etc.), purchase orderdata, invoice data, shipping data, receipt data, or any combinationthereof In certain embodiments, the transaction data 206 may includeblockchain-associated data, smart contract data, or any combinationthereof. Skilled practitioners of the art will realize that many suchexamples of transaction 206 data are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

As used herein, social 208 data broadly refers to information thatsocial media users publicly share, which may include metadata such asthe user's location, language spoken, biographical, demographic orsocio-economic information, and shared links. As likewise used herein,device 210 data broadly refers to data associated with, or generated by,an apparatus. Examples of device 210 data include data associated with,or generated by, a vehicle, home appliance, security systems, and soforth, that contain electronics, software, sensors, actuators, andconnectivity, or a combination thereof, that allow the collection,interaction and provision of associated data.

As used herein, ambient 212 data broadly refers to input signals, orother data streams, that may contain data providing additional insightor context to public 202, proprietary 204, transaction 206, social 208,and device 210 data received by the AIS 118. For example, ambientsignals may allow the AIS 118 to understand that a user is currentlyusing their mobile device, at location ‘x’, at time ‘y’, doing activity‘z’. To continue the example, there is a difference between the userusing their mobile device while they are on an airplane versus usingtheir mobile device after landing at an airport and walking between oneterminal and another.

To extend the example, ambient 212 data may add additional context, suchas the user is in the middle of a three leg trip and has two hoursbefore their next flight. Further, they may be in terminal A1, but theirnext flight is out of C1, it is lunchtime, and they want to know thebest place to eat. Given the available time the user has, their currentlocation, restaurants that are proximate to their predicted route, andother factors such as food preferences, the AIS 118 can perform variouscognitive operations and provide a cognitive insight 262 that includes arecommendation for where the user can eat.

To extend the example even further, the user may receive a notificationwhile they are eating lunch at a recommended restaurant that their nextflight has been canceled due to the previously-scheduled aircraft beinggrounded. As a result, the user may receive two cognitive insights 262suggesting alternative flights on other carriers. The first cognitiveinsight 262 may be related to a flight that leaves within a half hour.The second cognitive insight 262 may be related to a second flight thatleaves in an hour but requires immediate booking and payment ofadditional fees. Knowing that they would be unable to make the firstflight in time, the user elects to use the second cognitive insight 262to automatically book the flight and pay the additional fees through theuse of a digital currency transaction.

In certain embodiments, the AIS 118 may be implemented to representknowledge in the cognitive graph 260, such that the knowledge can beused to perform reasoning and inference operations. In certainembodiments, the resulting reasoning and inference operations may beimplemented to provide self-assurance. Accordingly, such approaches maybe implemented in certain embodiments as a cognitive inference andlearning system (CILS). In certain embodiments, the self-assuranceresulting from such reasoning and inference operations may beimplemented to provide cognitive insights 262 with associatedexplainability. In these embodiments, such explainability may beimplemented to provide a rationale for their associated cognitiveinsights 262.

As used herein, a cognitive graph 260 refers to a representation ofexpert knowledge, associated with individuals and groups over a periodof time, to depict relationships between people, places, and thingsusing words, ideas, audio and images. As such, it is a machine-readableformalism for knowledge representation that provides a common frameworkallowing data and knowledge to be shared and reused across user,application, organization, and community boundaries. In variousembodiments, the information contained in, and referenced by, acognitive graph 260 may be derived from many sources, such as public202, proprietary 204, transaction, social 208, device 210, and ambient212 data, or a combination thereof. In certain of these embodiments, thecognitive graph 260 may be implemented to assist in the identificationand organization of information associated with how people, places andthings are related to one other. In various embodiments, the cognitivegraph 260 may be implemented to enable automated cognitive agents 250,described in greater detail herein, to access the Web moreintelligently, enumerate inferences through utilization of various datasources, and provide answers to questions by serving as a computationalknowledge engine.

In certain embodiments, the cognitive graph 260 may be implemented tonot only elicit and map expert knowledge by deriving associations fromdata, but to also render higher level insights and accounts forknowledge creation through collaborative knowledge modeling. In certainembodiments, the cognitive graph 260 may be implements as amachine-readable, declarative memory system that stores and learns bothepisodic memory (e.g., specific personal experiences associated with anindividual or entity), and semantic memory, which stores factualinformation (e.g., geo location of an airport or restaurant).

For example, the cognitive graph 260 may know that a given airport is aplace, and that there is a list of related places such as hotels,restaurants and departure gates. Furthermore, the cognitive graph 260may know that people such as business travelers, families and collegestudents use the airport to board flights from various carriers, eat atvarious restaurants, or shop at certain retail stores. The cognitivegraph 260 may also have knowledge about the key attributes from variousretail rating sites that travelers have used to describe the food andtheir experience at various venues in the airport over the past sixmonths.

In various embodiments, the cognitive process orchestration platform 124may be implemented to orchestrate certain cognitive agents 250 togenerate one or more cognitive insights 262. In certain embodiments, theresulting cognitive insights 262 may be delivered to one or moredestinations 264, described in greater detail herein. As used herein, acognitive insight 262 broadly refers to an actionable, real-timerecommendation tailored to a particular user, as described in greaterdetail herein. Examples of such recommendations include getting animmunization, correcting a billing error, taking a bus to anappointment, considering the purchase of a particular item, selecting arecipe, eating a particular food item, and so forth.

In certain embodiments, cognitive insights 262 may be generated fromvarious data sources, such as public 202, proprietary 204, transaction,social 208, device 210, and ambient 212 data, a cognitive graph 260, ora combination thereof. For example, if a certain percentage of thepopulation in a user's community is suffering from the flu, then theuser may receive a recommendation to get a flu shot. In this example,determining the afflicted percentage of the population, or determininghow to define the community itself, may prove challenging. Accordingly,generating meaningful insights or recommendations may be difficult foran individual user, especially when related datasets are large.

In certain embodiments, a resulting cognitive insight 262 stream may beimplemented to be bidirectional, supporting flows of information bothtoo and from various destinations 264. In these embodiments, a firstflow of cognitive insights 262 may be generated in response to receivinga query, and subsequently delivered to one or more destinations 264.Likewise, a second flow of cognitive insights 262 may be generated inresponse to detecting information about a user of one or more of thedestinations 264.

Such use may result in the provision of information to the AIS 118. Inresponse, the AIS 118 may process that information, in the context ofwhat it knows about the user, and provide additional information to theuser, such as a recommendation. In certain embodiments, a stream ofcognitive insights 262 may be configured to be provided in a “push”stream configuration familiar to those of skill in the art. In certainembodiments, a stream of cognitive insights 262 may be implemented touse natural language approaches familiar to skilled practitioners of theart to support interactions with a user.

In certain embodiments, a stream of cognitive insights 262 may beimplemented to include a stream of visualized insights. As used herein,visualized insights broadly refer to cognitive insights that arepresented in a visual manner, such as a map, an infographic, images, andso forth. In certain embodiments, these visualized insights may includevarious cognitive insights, such as “What happened?”, “What do I knowabout it?”, “What is likely to happen next?”, or “What should I do aboutit?” In these embodiments, the stream of cognitive insights 262 may begenerated by various cognitive agents 250, which are applied to varioussources, datasets, and cognitive graphs.

In certain embodiments, the AIS 118 may be implemented to deliverCognition as a Service (CaaS). As such, it provides a cloud-baseddevelopment and execution platform that allow various cognitiveapplications and services to function more intelligently andintuitively. In certain embodiments, cognitive applications powered bythe AIS 118 are able to think and interact with users as intelligentvirtual assistants. As a result, users are able to interact with suchcognitive applications by asking them questions and giving themcommands. In response, these cognitive applications will be able toassist the user in completing tasks and managing their work moreefficiently.

In these and other embodiments, the AIS 118 may be implemented tooperate as an analytics platform to process big data, and dark data aswell, to provide data analytics through a public, private or hybridcloud environment, described in greater detail herein. As used herein,cloud analytics broadly refers to a service model wherein data sources,data models, processing applications, computing power, analytic models,and sharing or storage of results are implemented within a cloudenvironment to perform one or more aspects of analytics.

In certain embodiments, users may submit queries and computationrequests in a natural language format to the AIS 118. In response, theyare provided with a ranked list of relevant answers and aggregatedinformation with useful links and pertinent visualizations through agraphical representation. In these embodiments, the cognitive graph 230may be implemented to generate semantic and temporal maps to reflect theorganization of unstructured data and to facilitate meaningful learningfrom potentially millions of lines of text, much in the same way asarbitrary syllables strung together create meaning through the conceptof language.

In certain embodiments, the AIS 118 may be implemented to representknowledge in the cognitive graph 260, such that the knowledge can beused to perform reasoning and inference operations. In certainembodiments, the resulting reasoning and inference operations may beimplemented to provide self-assurance. Accordingly, such approaches maybe implemented in certain embodiments as a cognitive inference andlearning system (CILS). In certain embodiments, the self-assuranceresulting from such reasoning and inference operations may beimplemented to provide cognitive insights with associatedexplainability. In these embodiments, such explainability may beimplemented to provide a rationale for their associated cognitiveinsights.

FIG. 3 is a simplified block diagram of an augmented intelligence system(AIS) reference model implemented in accordance with an embodiment ofthe invention. In various embodiments, the AIS 118 reference model shownin FIG. 3 may be implemented as a reference for certain componentsincluded in, and functionalities performed by, an AIS 118, described ingreater detail herein. In certain embodiments, these components andfunctionalities may include a cognitive infrastructure 302, one or morecognitive Application Program Interfaces (APIs) 308, a cognitive processfoundation 310, various cognitive processes 320, and various cognitiveinteractions 328. In certain embodiments, the cognitive infrastructure302 may include various sources of multi-structured big data 304 and ahosted/private/hybrid cloud infrastructure, both of which are describedin greater detail herein.

In certain embodiments, the cognitive process foundation 310, likewisedescribed in greater detail herein, may be implemented to providevarious cognitive computing functionalities. In certain embodiments,these cognitive computing functionalities may include the simplificationof data and compute resource access 312. In certain embodiments, thesecognitive computing functionalities may include various sharing andcontrol 314 operations commonly associated with domain processes. Incertain embodiments, these cognitive computing functionalities mayinclude the composition and orchestration 316 of various artificialintelligence (AI) systems.

In certain embodiments, the composition and orchestration 316 of variousartificial intelligence (AI) systems may include the composition ofcognitive skills and cognitive agents, as described in greater detailherein. In certain embodiments, the composition and orchestration 316may include the orchestration of various cognitive agents, andassociated AIS components, to generate one or more cognitive processes,likewise described in greater detail herein. In certain embodiments,these cognitive computing functionalities may include governance andassurance 318 operations associated with ensuring AI performance in thecontext of various cognitive computing operations. In certainembodiments, these various cognitive computing functionalities may beimplemented to work individually, or in concert with one another. Inthese embodiments, the method by which these various cognitive computingfunctionalities is a matter of design choice.

As used herein, a cognitive process broadly refers to an instantiationof a cognitive computing operation, described in greater detail herein.In certain embodiments, the cognitive process 320 may be implemented asan intelligent user engagement 322. As used herein, an intelligent userengagement 322 broadly refers to the application of certain cognitiveoperations to assist in more meaningful cognitive interactions 328between a user and certain cognitive processes 320. In certainembodiments, the cognitive process 320 may be implemented as anaugmented process engagement 324. As used herein, an augmented processengagement broadly refers to the application of cognitive operations toimprove interaction between certain cognitive processes 320. In certainembodiments, the cognitive process 320 may be implemented as one or morecognitive applications 326, described in greater detail herein. Incertain embodiments, cognitive interactions 328 may be implemented tosupport user interactions with an AIS 118 through web 330 applications,mobile 332 applications, chatbot 334 interactions, voice 336interactions, augmented reality (AR) and virtual reality (VR)interactions 338, or a combination thereof

FIG. 4 is a simplified block diagram of an augmented intelligence system(AIS) platform implemented in accordance with an embodiment of theinvention. In certain embodiments, the AIS platform may be implementedto include various cognitive processes 320, a cognitive processfoundation 310, various Cognitive Application Program Interfaces (APIs)308, and an associated cognitive infrastructure 302. In certainembodiments, the cognitive processes 320 may be implemented tounderstand and adapt to the user, not the other way around, by nativelyaccepting and understanding human forms of communication, such asnatural language text, audio, images, video, and so forth.

In these and other embodiments, the cognitive processes 320 may beimplemented to possess situational and temporal awareness based uponambient signals from users and data, which facilitates understanding theuser's intent, content, context and meaning to drive goal-driven dialogsand outcomes. Further, they may be designed to gain knowledge over timefrom a wide variety of structured, non-structured, transactional, anddevice data sources, continuously interpreting and autonomouslyreprogramming themselves to better understand a given domain. As such,they are well-suited to support human decision making, by proactivelyproviding trusted advice, offers and recommendations while respectinguser privacy and permissions.

In certain embodiments, the cognitive processes 320 may be implementedin concert with a cognitive application framework 442. In certainembodiments, the cognitive processes 320 and the cognitive applicationframework 442 may be implemented to support plug-ins and components thatfacilitate the creation of various cognitive applications 326, describedin greater detail herein. In certain embodiments, the cognitiveprocesses 320 may be implemented to include widgets, user interface (UI)components, reports, charts, and back-end integration componentsfamiliar to those of skill in the art.

As likewise shown in FIG. 4, the cognitive process foundation 310 may beimplemented in certain embodiments to include a cognitive processorchestration platform 124, a cognitive process composition platform122, and various cognitive agents 250, all of which are described ingreater detail herein. In certain embodiments, the cognitiveorchestration platform 124 may be implemented to orchestrate variouscognitive agents 250 to enable one or more cognitive processes 320. Incertain embodiments, the cognitive orchestration platform 124 may beimplemented to manage accounts and projects, along with user-specificmetadata that is used to drive processes and operations within thecognitive process foundation 310 for a particular project.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to compose one or more cognitive agents 250. In certainembodiments, the cognitive agents 250 may include a sourcing agent 432,a destination agent 434, an engagement agent 436, a compliance agent438, or a combination thereof. In certain embodiments, the sourcingagent 432 may be implemented to source a variety of multi-site,multi-structured source streams of data described in greater detailherein.

In various embodiments, the destination agent 436 may be implemented topublish cognitive insights to a consumer of cognitive insight data.Examples of such consumers of cognitive insight data include targetdatabases, public or private blockchains, business intelligenceapplications, and mobile applications. It will be appreciated that manysuch examples of cognitive insight data consumers are possible. Incertain embodiments, the engagement agents 436 may be implemented todefine various cognitive interactions 328 between a user and aparticular cognitive process 320. In certain embodiments, the complianceagents 438 may be implemented to ensure compliance with certain businessand technical guidelines, rules, regulations or other parametersassociated with an organization.

In certain embodiments, the resulting cognitive agents 250 may beorchestrated by the cognitive process orchestration platform 124 tocreate cognitive insights, described in greater detail herein. Incertain embodiments, the resulting cognitive agents 250 may beorchestrated by the cognitive process orchestration platform 124 tocreate custom extensions to the AIS 118 shown in FIG. 2. In certainembodiments, the cognitive process foundation 310 may be implemented forthe development of a cognitive application 326, which may subsequentlybe deployed in a public, private or hybrid cloud 306 cloud environment.

In various embodiments, the APIs 308 may be implemented for use by thecognitive process orchestration platform 124 to orchestrate certaincognitive agents 250, described in greater detail herein, which are thenexecuted by the AIS 118 to generate cognitive insights. In certainembodiments, the APIs 308 may be implemented to access various cognitiveinfrastructure 302 components. In certain embodiments, theinfrastructure components may include repositories of multi-structuredbig data 304, a hosted/private/hybrid cloud 306 environment, or both. Incertain embodiments, the repositories of multi-structured big data 304may be accessed by the AIS platform to generate cognitive insights.

In certain embodiments, the repositories of multi-structured big data304 may include individual repositories of public 202, proprietary 204,transaction 206, social 208, device 210, and ambient 212 data, or somecombination thereof. In certain embodiments, the repositories oftransaction data 206 may include blockchain data associated with one ormore public blockchains, one or more private blockchains, or acombination thereof. In certain embodiments, the repositories oftransaction data 206 may be used to generate a blockchain-associatedcognitive insight.

In certain embodiments, as described in greater detail herein, thecognitive infrastructure 302 environment may include variousinput/output services 404, described in greater detail herein, acognitive cloud management 406 platform, and various cognitive cloudanalytics 408 components, or a combination thereof. In certainembodiments, hosted/private/hybrid cloud 306 may include a repository ofcognitive process components 402. In certain embodiments, the repositoryof cognitive process components 402 may be implemented to storecognitive agents, cognitive skills, cognitive models, cognitivealgorithms, and cognitive actions.

In various embodiments, the contents of the cognitive process components402 may be used by the cognitive skill composition platform 122 tocompose certain cognitive skills. In various embodiments, the contentsof the cognitive process components 402 may be used by the cognitiveagent composition platform 120 to compose certain cognitive agents. Invarious embodiments, the contents of the cognitive process components402 may be used by the cognitive process orchestration platform 124 toorchestrate certain cognitive processes 320.

FIG. 5 shows a simplified block diagram of components associated with acognitive process foundation implemented in accordance with anembodiment of the invention. In certain embodiments, the cognitiveprocess foundation 310 may be implemented to include a cognitive agentcomposition platform 120 and a cognitive process orchestration platform124. In certain embodiments, the cognitive agent composition platform120 may be implemented to include a cognitive skill composition platform122, described in greater detail herein, and a cognitive agentcomposition User Interface (UI) 522, likewise described in greaterdetail herein.

In various embodiments, the cognitive skill composition platform 122 maybe implemented to perform certain operations associated with thecomposition of a particular cognitive skill. In certain embodiments, thecognitive skill composition 530 operations may include the development,testing, and definition of a cognitive skill, as described in greaterdetail herein. In certain embodiments, the cognitive skill composition530 operations may include the development of one or more cognitivealgorithms, as likewise described in greater detail herein. In certainembodiments, the cognitive skill composition 530 operations may includethe definition of various cognitive model actions. In certainembodiments, the cognitive skill composition 530 operations may includethe identification of data sources, such as the public 202, proprietary204, transaction, social 208, device 210, and ambient 212 data sourcesdescribed in the descriptive text associated with FIG. 2. In certainembodiments, the cognitive skill composition 530 operations may includethe definition of required datasets, described in greater detail herein.

In certain embodiments, the cognitive skill composition platform 122 maybe implemented with an associated cognitive skill client library 540 andone or more cognitive skill Application Program Interfaces (APIs) 550.In certain embodiments, the cognitive skill client library 540, and oneor more cognitive skill Application Program Interfaces (APIs) 550, maybe implemented by the cognitive skill composition platform 122 tocompose a particular cognitive skill.

In certain embodiments, the cognitive agent composition UI 522 may beimplemented to provide a visual representation of the execution ofindividual operations associated with the composition of a particularcognitive agent. In certain embodiments, the cognitive agent compositionoperations 532 may include the development of various datasets used by aparticular cognitive agent during its execution. In various embodiments,the cognitive agent composition operations 532 may include the curationand uploading of certain training data used by a cognitive modelassociated with a particular cognitive agent. In certain embodiments,the development of the various datasets and the curation and uploadingof certain training data may be performed via a data engineeringoperation.

In certain embodiments, the cognitive agent composition operations 532may include creation of a cognitive agent record. In certainembodiments, the cognitive agent record may be implemented by anaugmented intelligence system (AIS) to track a particular cognitiveagent. In certain embodiments, the cognitive agent record may beimplemented by an AIS to locate and retrieve a particular cognitiveagent stored in a repository of AIS components, described in greaterdetail herein. In certain embodiments, the cognitive agent compositionoperations 532 may include the addition, and configuration of, one ormore cognitive skills associated with a particular cognitive agent.

In certain embodiments, the cognitive agent composition operations 532may include the definition of various input/output services, describedin greater detail herein, associated with a particular cognitive agent.In certain embodiments, the cognitive agent composition operations 532may include the definition of various dataset connections associatedwith a particular cognitive agent. In certain embodiments, thedefinition of various dataset connections may be performed via a dataengineering operation. In certain embodiments, the cognitive agentcomposition operations 532 may include the creation of one or more dataflows associated with a particular cognitive agent. In certainembodiments, the cognitive agent composition operations 532 may includethe mapping of one or more data flows associated with a particularcognitive agent. In certain embodiments, the mapping of data flows maybe performed via a date engineering operation. In certain embodiments,the cognitive agent composition operations 532 may include the testingof various services associated with a particular cognitive agent.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented with an associated cognitive agent client library 542 andone or more cognitive agent APIs 552. In certain embodiments, thecognitive agent library 542, and one or more cognitive agent APIs 552,may be implemented by the cognitive agent composition platform 120 tocompose a particular cognitive agent.

In certain embodiments, the cognitive process foundation 310 may beimplemented to include a cognitive process orchestration platform 124.In certain embodiments, the cognitive process orchestration platform 124may be implemented to include an AIS administration console 524, an AIScommand line interface (CLI) 526, or both. In certain embodiments, theAIS administration console 524 may be implemented as a Graphical UserInterface (GUI).

In certain embodiments, the AIS administration console 524 and the AISCLI 526, individually or in combination, may be implemented to managethe building blocks of a particular cognitive process, described ingreater detail herein. In certain embodiments, the building blocks of aparticular cognitive process may include one or more cognitive agents,likewise described in greater detail herein. In certain embodiments, theAIS administration console 524 and the AIS CLI 526, individually or incombination, may be implemented to manage lifecycle of a cognitiveagent, described in greater detail herein.

In certain embodiments, the AIS administration console 524 may beimplemented to manage a cognitive process user account. In certainembodiments, the AIS administration console 524 may be implemented viewvarious AIS logs and metrics. In certain embodiments, the AISadministration console 524 may be implemented as a web interfacefamiliar to those of skill in the art.

In certain embodiments, the AIS CLI 526 may be implemented to generateand deploy cognitive skills, created and save dataset definitions,invoke cognitive agent services, and configure cognitive action batchjobs and connections, or a combination thereof. In certain embodiments,the AIS CLI 526 may be implemented to add cognitive agent buildingblocks to the cognitive agent composition platform 120. In certainembodiments, the AIS CLI 526 may be implemented to execute cognitiveagent lifecycle commands.

In certain embodiments, the AIS administration console 524 and the AISCLI 526, individually or in combination, may be implemented to performvarious data orchestration 534 operations. In certain embodiments, thedata orchestration 534 operations may include the definition of datasources associated with a particular AIS region, described in greaterdetail herein. In certain embodiments, the data orchestration 534operations may include the definition of various data variablesassociated with a particular AIS region.

In certain embodiments, the AIS administration console 524 and the AISCLI 526, individually or in combination, may be implemented to performvarious cognitive agent orchestration 536 operations. In certainembodiments, the cognitive agent orchestration 536 operations mayinclude the creation of a cognitive agent snapshot. As used herein, acognitive agent snapshot broadly refers to a depiction of theoperational state of a cognitive agent at a particular instance in timeduring the execution of a cognitive process.

In certain embodiments, the cognitive agent orchestration 536 operationsmay include the promotion of a cognitive agent snapshot. As likewiseused herein, promotion broadly refers to the transition of a cognitiveagent, or a cognitive process, from one operational environment toanother. As an example, the cognitive process orchestration platform 124may be implemented in a development environment to generate a cognitiveprocess by orchestrating certain cognitive agents, as described ingreater detail herein. Once development has been completed, theresulting cognitive process may be promoted to a test environment.Thereafter, once testing of the cognitive process has been completed, itmay be promoted to a user acceptance environment, and once the useracceptance phase has been completed, it may be promoted to a productionenvironment.

In certain embodiments, the cognitive agent orchestration 536 operationsmay include the creation of a cognitive agent instance. In certainembodiments, the cognitive agent orchestration 536 operations mayinclude enablement of start triggers for a particular cognitive agent.In certain embodiments, the cognitive agent orchestration 536 operationsmay include the invocation of a particular instance of a cognitiveagent. In certain embodiments, the cognitive agent orchestration 536operations may include querying and filtering responses received from aparticular cognitive agent. In certain embodiments, the cognitiveprocess orchestration platform 124 may be implemented with an associatedAIS console client library 544, one or more AIS console APIs 554, an AISCLI client library 546, one or more AIS CLI APIs 556, or a combinationthereof.

FIG. 6 is a simplified block diagram of a plurality of augmentedintelligence system (AIS) platforms implemented in accordance with anembodiment of the invention within a hybrid cloud infrastructure. Incertain embodiments, the hybrid cloud infrastructure 304 may beimplemented to include a cognitive cloud management 402 platform, ahosted 602 cognitive cloud environment, and a private 622 cognitivecloud environment. In certain embodiments, the private 622 cognitivecloud environment may be implemented in a private network, such ascommonly implemented by corporation or government organization.

In certain embodiments, the hosted 602 and private 622 cognitive cloudenvironment may respectively be implemented to include a hosted 604 andprivate 624 AIS platform. Likewise, in certain embodiments, the hosted604 and private 624 AIS platforms may respectively be implemented toinclude one or more hosted 626 and private 626 AIS regions. As usedherein, a hosted 606 AIS region broadly refers to a designatedinstantiation of an AIS implemented to execute on a corresponding hosted604 platform. As likewise used herein, a private 626 AIS region broadlyrefers to a designated instantiation of an AIS implemented to execute ona corresponding private 624 platform. In certain embodiments, thedesignated instantiation of a hosted 606 or private 626 AIS region maybe defined by a set of associated parameters.

As an example, the designation parameters associated with a hosted 606or private 626 AIS region, individually or in combination, may tocorrespond to a defined geographic area. To continue the example, thedesignation parameters associated with a particular hosted 606 AISregion may correspond to certain defining information associated withthe state of Texas. Likewise, the designation parameters associated witha first and second private 626 AIS region may respectively correspond tocertain defining information associated with Dallas and Harris counties,both of which are located in the state of Texas. In this example, thehosted 606 AIS region may be implemented to provide various cognitiveinsights related to the state government of Texas to various countygovernments. Likewise, the first and second private 626 AIS regions maybe respectively implemented to provide cognitive insights specific tothe county governments of Dallas and Harris counties.

As another example, the designation parameters associated with a hosted606 or private 626 AIS region, individually or in combination, may tocorrespond to various aspects of an organization. To continue theexample, the designation parameters associated with a particular hosted606 AIS region may correspond to certain defining information associatedwith an automobile dealer network. Likewise, the designation parametersassociated with a first and second private 626 AIS region mayrespectively correspond to certain defining information associated withtwo independent automobile dealers, both of which are located in thesame city and sell the same brand of automobiles. In this example, thehosted 606 AIS region may be implemented to provide various cognitiveinsights related to certain aspects of the automobile brand. Likewise,the first and second private 626 AIS regions may be respectivelyimplemented to provide cognitive insights specific to certain aspects ofthe two automobile dealers, such as their respective inventories,customer demographics, and past promotional activities.

In certain embodiments, each hosted 606 and private 626 AIS regions maybe implemented to include one hosted 608 or private 628 AISenvironments. As used herein, a hosted 608 AIS environment broadlyrefers to an operating environment within which a particular hosted 608AIS environment is implemented. As likewise used herein, a privatehosted 628 AIS environment broadly refers to an operating environmentwithin which a particular private 628 AIS environment is implemented.

As an example, a cognitive process may first be implemented in a hosted608 or private 628 development environment to generate a cognitiveprocess by orchestrating certain cognitive agents, as described ingreater detail herein. Once development has been completed, theresulting cognitive process may be promoted to a hosted 608 or private628 test environment. Thereafter, once testing of the cognitive processhas been completed, it may be promoted to a hosted 608 or private 628user acceptance environment. Likewise, once the user acceptance phasehas been completed, it may be promoted to a hosted 608 or private 628production environment.

In certain embodiments, each hosted 608 and private 628 AIS environmentsmay be implemented to include they use of one or more hosted 610 orprivate 630 cognitive agents, described in greater detail herein, togenerate cognitive insights, likewise described in greater detailherein. In certain embodiments, a gateway/load balancer 644 may beimplemented to allow the hosted 604 and private 624 AIS platforms tocommunicate with one another. In certain embodiments, the ability tocommunicate with one another allows the hosted 604 and private 624 AISplatforms to work collaboratively when generating cognitive insightsdescribed in greater detail herein.

FIG. 7 shows components of a plurality of augmented intelligence system(AIS) platforms implemented in accordance with an embodiment of theinvention within a hosted/private/hybrid cloud environment. In certainembodiments, the hybrid cloud infrastructure 304 may be implemented toinclude a cognitive cloud management 406 platform, a hosted 702cognitive container management infrastructures, and a private 722cognitive container management infrastructure. In certain embodiments,the hosted 702 and private 722 cognitive container managementinfrastructures may be implemented to respective include one or morevirtual machines (VMs) ‘1’ 704 through ‘n’ 706 and VMs ‘1 724 through‘n’ 726.

In certain embodiments, the hybrid cloud infrastructure 304 may likewisebe implemented to include hosted 708 and private 728 cognitive servicesinfrastructures, hosted 716 and private 736 cognitive computeinfrastructures, and a gateway/load balancer 644. In certainembodiments, the hosted 708 and private 728 cognitive servicesinfrastructures may be implemented to respective include one or morevirtual machines (VMs) ‘1’ 710 through ‘n’ 712 and VMs ‘1 730 through‘n’ 732. In certain embodiments, the hosted 716 and private 736cognitive compute infrastructures may likewise be implemented torespective include one or more virtual machines (VMs) ‘1’ 718 through‘n’ 720 and VMs ‘1 738 through ‘n’ 740.

Likewise, in certain embodiments the hybrid cloud infrastructure 304 maybe implemented to include various repositories of hosted 714 and private734 data. As used herein, a repository of hosted 714 or private 734 databroadly refers to a collection of knowledge elements that can be used incertain embodiments to generate one or more cognitive insights,described in greater detail herein. In certain embodiments, theseknowledge elements may include facts (e.g., milk is a dairy product),information (e.g., an answer to a question), descriptions (e.g., thecolor of an automobile), abilities (e.g., the knowledge of how toinstall plumbing fixtures), and other classes of knowledge familiar tothose of skill in the art. In these embodiments, the knowledge elementsmay be explicit or implicit. As an example, the fact that water freezesat zero degrees centigrade is an explicit knowledge element, while thefact that an automobile mechanic knows how to repair an automobile is animplicit knowledge element.

In certain embodiments, the knowledge elements within a repository ofhosted 714 or private 734 data may also include statements, assertions,beliefs, perceptions, preferences, sentiments, attitudes or opinionsassociated with a person or a group. As an example, user ‘A’ may preferthe pizza served by a first restaurant, while user ‘B’ may prefer thepizza served by a second restaurant. Furthermore, both user ‘A’ and ‘B’are firmly of the opinion that the first and second restaurantsrespectively serve the very best pizza available. In this example, therespective preferences and opinions of users ‘A’ and ‘B’ regarding thefirst and second restaurant may be included in a universal knowledgerepository as they are not contradictory. Instead, they are simplyknowledge elements respectively associated with the two users and can beused in various embodiments for the generation of certain cognitiveinsights, as described in greater detail herein.

In certain embodiments, individual knowledge elements respectivelyassociated with the repositories of hosted 714 and private 734 data maybe distributed. In certain embodiments, the distributed knowledgeelements may be stored in a plurality of data stores familiar to skilledpractitioners of the art. In certain embodiments, distributed knowledgeelements may be logically unified for various implementations of therepositories of hosted 714 and private 734 data.

In certain embodiments, the repositories of hosted 714 and private 734data may be respectively implemented in the form of a hosted or privateuniversal cognitive graph, described in greater detail herein. Incertain embodiments, individual nodes within a hosted or privateuniversal cognitive graph may contain one or more knowledge elements. Incertain embodiments, the repositories of hosted 714 and private 734 datamay be respectively implemented to include a repository of hosted andprivate AIS components, such as the AIS component repository 402 shownin FIG. 4 and described in its associated descriptive text.

In certain embodiments, the repositories of hosted 714 and private 734data may respectively include one or more repositories of applicationdata, proprietary data, and proprietary transaction data. In certainembodiments, the repositories of hosted or private transaction data mayinclude credit or debit card transaction data, financial services dataof all kinds (e.g., mortgages, insurance policies, stock transfers,etc.), purchase order data, invoice data, shipping data, receipt data,or any combination thereof. In various embodiments, the repositories ofhosted or private transaction data may likewise includeblockchain-associated data, smart contract data, or any combinationthereof.

In certain embodiments, hosted and private transaction data may beexchanged through the implementation of a transaction data exchangeimplemented on the gateway/load balancer 644. In certain embodiments,the implementation of such a transaction data exchange may allow thehosted 716 cognitive compute infrastructure to access certain privatetransaction data. Conversely, the private 736 cognitive computeinfrastructure may be allowed to access certain hosted transaction data.In certain embodiments, the transaction data exchange may be implementedwith permission and identity management controls to determine the degreeto which certain hosted and private transaction data may be respectivelyaccessed by the hosted 716 and private 736 cognitive computeinfrastructures.

In certain embodiments, the repositories of hosted or privatetransaction data may include data associated with a public blockchain.As used herein, a public blockchain broadly refers to a blockchain thathas been implemented as a permissionless blockchain, meaning anyone canread or write to it. One advantage of such a public blockchain is itallows individuals who do not know each other to trust a shared recordof events without the involvement of an intermediary or third party.

In certain embodiments, a repository of private transaction data may beimplemented to include data associated with a proprietary blockchain. Aslikewise used herein, a proprietary blockchain broadly refers to ablockchain where its participants are known and are granted read andwrite permissions by an authority that governs the use of theblockchain. For example, proprietary blockchain participants may belongto the same or different organizations within an industry sector. Incertain embodiments, these relationships may be governed by informalrelationships, formal contracts, or confidentiality agreements.

Skilled practitioners of the art will recognize that while manytransactions may benefit from the decentralized approach typicallyimplemented by a public blockchain, others are more suited to beinghandled by an intermediary. Such intermediaries, while possibly addingadditional complexities and regulation, can often provide demonstrablevalue. In certain embodiments, an intermediary associated with aproprietary blockchain may have the ability to veto or rescind suspecttransactions, provide guarantees and indemnities, and deliver variousservices not generally available through a public blockchain.

Furthermore, proprietary blockchains have several advantages, includingthe use of cryptographic approaches known to those of skill in the artfor identity management and verification of transactions. Theseapproaches not only prevent the same transaction taking place twice,such as double-spending a digital currency, they also provide protectionagainst malicious activities intended to compromise a transaction bychanging its details. Moreover, permission controls typically associatedwith proprietary blockchains can provide dynamic control over who canconnect, send, receive and enact individual transactions, based upon anynumber of parameters that may not be available or implementable inpublic blockchains. Accordingly, full control can be asserted over everyaspect of a proprietary blockchain's operation, not only in accordancewith the consensus of its various participants, but its administrativeintermediary as well.

In certain embodiments, the hosted 708 or private 728 cognitive servicesinfrastructure may be implemented to manage the identity of a user,group or organization in the performance of blockchain-associatedcognitive insight operations. In certain embodiments, the hosted 708 orprivate 728 cognitive services infrastructure may be implemented toperform various cognitive identity management operations. In certainembodiments, the cognitive identity management operations may includethe use of cognitive personas, cognitive profiles, or a combinationthereof, to perform blockchain-associated cognitive insight operationsassociated with a particular user, group or organization. In certainembodiments, the cognitive identity management operations may beimplemented to verify the identity of a user, group or organization inthe performance of a blockchain-associated cognitive insight operation.

In certain embodiments, the cognitive identity management operations maylikewise involve the generation, and ongoing management of, privatekeys, shared keys, public/private key pairs, digital signatures, digitalcertificates, or any combination thereof, associated with a particularuser, group or organization. Likewise, in certain embodiments thecognitive identity management operations may involve the encryption ofone or more cognitive insights, one or more smart contracts, or somecombination thereof, during the generation of a blockchain-associatedcognitive insight. Those of skill in the art will recognize that manysuch embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the gateway/load balancer 644 may be implementedfor the hosted 708 cognitive services infrastructure provide certainhosted data and knowledge elements to the private 728 cognitive servicesinfrastructure, In certain embodiments, the provision of certain hosteddata and knowledge elements allows the hosted 714 repository of data tobe replicated as the private 724 repository of data. In certainembodiments, the provision of certain hosted data and knowledge elementsto the private 728 cognitive services infrastructure allows the hosted714 repository of data to provide updates to the private 734 repositoryof data. In certain embodiments, the updates to the private 734repository of data do not overwrite other data. Instead, the updates aresimply added to the private 734 repository of data.

In certain embodiments, knowledge elements and data that are added tothe private 734 repository of data are not respectively provided to thehosted 714 repository of data. As an example, an airline may not wish toshare private information related to its customer's flights, the pricepaid for tickets, their awards program status, and so forth. In variousembodiments, certain knowledge elements and data that are added to theprivate 724 repository of data may be provided to the hosted 714repository of data. As an example, the owner of the private 734repository of data may decide to license certain knowledge elements anddata to the owner of the hosted 714 repository of data. To continue theexample, certain knowledge elements or data stored in the private 734repository of data may be anonymized prior to being provided forinclusion in the hosted 714 repository of data.

In certain embodiments, only private knowledge elements or data arestored in the private 734 repository of data. In certain embodiments,the private 736 cognitive compute infrastructure may use knowledgeelements and data stored in both the hosted 714 and private 734repositories of data to generate cognitive insights. Skilledpractitioners of the art will recognize that many such embodiments arepossible. Accordingly, the foregoing is not intended to limit thespirit, scope or intent of the invention.

FIGS. 8a and 8b are a simplified process diagram showing the performanceof cognitive process promotion operations implemented in accordance withan embodiment of the invention. In various embodiments, a cognitiveprocess foundation 310, described in greater detail herein, may beimplemented to perform certain cognitive process promotion 828operations associated with promoting a particular cognitive process fromone operating environment to another.

As shown in FIG. 8a , certain operations may be performed in a datasourcing 804 phase, which results in the generation various data sets806, which are then used in a machine learning (ML) model development808 phase. In turn, the resulting ML model may be incorporated into oneor more cognitive skills 810, which are then used in a cognitive agentdevelopment 812 phase to generate various cognitive agents 814.

The resulting cognitive agents 814, as shown in FIG. 8b , may then beimplemented in a cognitive agent deployment and management 816 phase,which results in certain feedback 818. In turn, the feedback 818 may beused in a cognitive agent measurement and performance 820 phase, whichresults in the generation of various cognitive insights 822. Theresulting cognitive insights 822 may then be used in a cognitive agentgovernance and control 826 phase.

FIG. 9 is a simplified process diagram showing phases of a cognitiveprocess lifecycle implemented in accordance with an embodiment of theinvention. As used herein, a cognitive process lifecycle broadly refersto a series of phases, or individual operational steps, or a combinationthereof, associated with a cognitive process, spanning its inception,development, implementation, testing, acceptance, production, revision,and eventual retirement. In certain embodiments, each phase oroperational step of a cognitive process lifecycle may have associatedinput artifacts, roles or actors, and output artifacts.

As used herein, an input artifact broadly refers to an article ofinformation used to perform an operation associated with completion of acertain phase, or performance of certain operational steps, of acognitive process lifecycle. Examples of input artifacts includearticles of information related to business and technical ideas, goals,needs, structures, processes, and requests. Other examples of inputartifacts include articles of information related to market andtechnical constraints, system architectures, and use cases. Yet otherexamples of input artifacts include articles of information related todata sources, previously-developed technology components, andalgorithms.

As likewise used herein, a role or actor broadly refers to a particularuser, or certain functions they may perform, participating in certainphases or operational steps of a cognitive process lifecycle. Examplesof roles or actors include business owners, analysts, and partners, userexperience (UX) and user interface (UI) designers, and project managers,as well as solution, enterprise and business process architects, Otherexamples of roles or actors include data scientists, machine learning(ML) engineers, data, integration and software engineers, as well assystem administrators.

An output artifact, as likewise used herein, broadly refers to anarticle of information resulting from the completion of a certain phase,or performance of certain operational steps, of a cognitive processlifecycle. Examples of output artifacts includeStrength/Weaknesses/Opportunity/Threat (SWOT) analysis results, KeyPerformance Indicator (KPI) definitions, and project plans. Otherexamples of output artifacts include use case models and documents,cognitive application UX and UI designs, and project plans. Yet otherexamples of output artifacts include dataset, algorithm, machinelearning (ML) model, cognitive skill, and cognitive agentspecifications, as well as their corresponding datasets, algorithms, MLmodels, cognitive skills, and cognitive agents. Those of skill in theart will recognize that many examples of input artifacts, roles oractors, and output artifacts are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In this embodiment, a cognitive process lifecycle is begun in step 902,followed by determining certain operational and performance parametersrelated to an associated cognitive process in step 904. The resultingoperational and performance parameters are then used in step 906 for usein various business analysis and planning purposes, described in greaterdetail herein. Information security and audibility issues associatedwith the cognitive process are then identified and addressed in step908, followed by reviews of the existing system and cognitivearchitecture, and any resulting updates, being performed in step 910.Likewise, the user experience (UX) and one or more user interfaces (UIs)associated with the cognitive process are respectively developed insteps 912 and 914.

Thereafter, solution realization operations, described in greater detailherein, are performed in step 916 to identify requirements and generatespecifications associated with data sourcing 918 and cognitive agentdevelopment 926 phases of the cognitive process lifecycle. Once solutionrealization operations are completed in step 916, data sourcing 918operations are begun in step 920 with the performance of various datadiscovery operations, described in greater detail herein. In certainembodiments, the data discovery operations may be performed by accessingvarious multi-structured, big data 304 sources, likewise described ingreater detail herein. Once the data discovery operations have beencompleted, then certain data engineering operations are performed instep 922 to prepare the sourced data for use in the cognitive agentdevelopment 926 phase. As used herein, data engineering refers toprocesses associated with data collection and analysis as well asvalidation of the sourced data. In various embodiments, the dataengineering operations may be performed on certain of themulti-structured, big data 304 sources.

Once the data sourcing 918 phase has been completed, the cognitive agentdevelopment 926 phase is begun in step 928 with development of one ormore machine learning (ML) models associated with the cognitive process.Any cognitive skills associated with the cognitive process that may notcurrently exist are composed in step 930. In certain embodiments, an MLmodel developed in step 928 may be used to compose a cognitive skill instep 930. Associated cognitive process components are then acquired instep 932 and used in step 934 to compose a cognitive agent. Theforegoing steps in the cognitive agent development 926 phase are theniteratively repeated until all needed cognitive agents have beendeveloped.

Once the cognitive agent development 926 phase has been completed,quality assurance and user acceptance operations associated with thecognitive process are respectively performed in step 936 and 938. Thecognitive process is then promoted, as described in greater detailherein, into a production phase in step 940. Once the cognitive processis operating in a production phase, ongoing system monitoring operationsare performed in step 942 to collect certain performance data. Theperformance data resulting from the monitoring operations performed instep 942 is then used in step 944 to perform various Key PerformanceIndicator (KPI) evaluation operations.

In turn, the results of the KPI evaluations are then used as feedback toimprove the performance of the cognitive process. In certainembodiments, the results of the KPI evaluations may be provided as inputin step 904 to determine additional operational and performanceparameters related to the cognitive process. In certain embodiments,these additional operational and performance parameters may be used torepeat one or more steps associated with the lifecycle of the cognitiveprocess to revise its functionality, improve its performance, or both.

FIGS. 10a through 10f show operations performed in a cognitive processlifecycle implemented in accordance with an embodiment of the invention.In this embodiment, a cognitive process lifecycle is begun in step 902,followed by determining certain operational and performance parametersrelated to an associated cognitive process in step 904. In certainembodiments, the operational and performance parameters determined instep 904 may include parameters related to business and technicalprocesses 1001, ideas 1002, requests 1003, needs 1104, and constraints1105, or a combination thereof

In certain embodiments, the operational and performance parametersresulting from step 904 may then be used for various business analysisand planning purposes in step 906. In certain embodiments, the businessand planning purposes may include understanding existing business andtechnical processes 1006. In certain embodiments, the business andplanning purposes may include understanding business and technical goalsand metrics 1007. In certain embodiments, the business and planningpurposes may include analyzing business and technical pain points,return on investment (ROI), user value, technical value, and processautomation, or a combination thereof 1008.

In certain embodiments, the business and planning purposes may includeassessing business and technical fit of use cases and proposed solutions1009. In certain embodiments, the business and planning purposes mayinclude prioritizing use cases and defining Key Performance Indicators(KPIs) 1010. In certain embodiments, the business and planning purposesmay include development of a project plan 1011.

In certain embodiments, information security and audibility issuesassociated with the cognitive process may be identified and addressed instep 908. In certain embodiments, the information security andauditability issues may include defining roles and resources 1012,establishing access policies 1013, updating security policies 1014, andreviewing code for vulnerabilities 1015, or a combination thereof. Incertain embodiments, the information security and auditability issuesmay include updating log access policies 1016, establishing patch andupdate policies 1017, and updating incidence response 1018 and disasterrecovery 1019 plans, or a combination thereof.

In certain embodiments, reviews of the existing system and cognitivearchitecture, and any resulting updates, may be performed in step 910.In certain embodiments, reviews of the existing system and cognitivearchitecture, and any resulting updates, may include developing anarchitectural vision for a proposed cognitive process 1020. In certainembodiments, reviews of the existing system and cognitive architecture,and any resulting updates, may include updating certain business andcognitive process architectures 1021. In certain embodiments, reviews ofthe existing system and cognitive architecture, and any resultingupdates, may include updating certain data and technology architectures1022.

In certain embodiments, the user experience (UX) and one or more userinterfaces (UIs) associated with the cognitive process may berespectively developed in steps 912 and 914. In certain embodiments,development of the UX design may include interviewing user to understandissues 1023 associated with the cognitive process. In certainembodiments, development of the UX design may include analyzing usersand building user personas 1024 associated with the cognitive process.In certain embodiments, development of the UX design may includeestablishing user performance objectives 1025 associated with thecognitive process.

In certain embodiments, development of the UX design may includecreating user stories and scenario maps 1026 associated with thecognitive process. In certain embodiments, development of the UX designmay include the creation of one or more visual designs 1027 associatedwith the cognitive process. In certain embodiments, development of theUX design may include testing UX designs associated with the cognitiveprocess with actual users 1029. In certain embodiments, development ofthe UX design may include validating design of the UX associated withthe cognitive process with usability tests 1030.

In certain embodiments, development of the UI may include reviewing theUX design 1031 associated with the cognitive process. In certainembodiments, development of the UI may include building or assembling aUI widget library 1032 associated with the cognitive process. In certainembodiments, development of the UI may include reviewing the backendApplication Program Interface (API) associated with the cognitiveprocess. In certain embodiments, development of the UI may includedeveloping one or more UIs associated with the cognitive process.

In certain embodiments, solution realization operations may be performedin step 916 to identify requirements and generate specificationsassociated with data sourcing 918 and cognitive agent development 926phases of the cognitive process lifecycle. In certain embodiments, thesolution realization operations may include identification of datasources 1035 relevant to the cognitive process. In certain embodiments,the solution realization operations may include the creation ofspecifications for datasets 1036 required by the cognitive process. Incertain embodiments, the solution realization operations may include thedefinition of various cognitive agents 1037 associated with thecognitive process.

In certain embodiments, the solution realization operations may includethe decomposition of one or more cognitive agents into correspondingcognitive skills 1038 associated with the cognitive process. In certainembodiments, the solution realization operations may include identifyingvarious cognitive skills based upon functional requirements 1039associated with the cognitive process. In certain embodiments, thesolution realization operations may include discovery of missingcognitive skills 1040 associated with the cognitive process. In certainembodiments, the solution realization operations may include creatingspecifications for missing cognitive skills 1041 associated with thecognitive process.

In certain embodiments, the data sourcing 918 phase may be initiated instep 920 with the performance of various data discovery operations. Incertain embodiments, the data discovery operations may include variousdata exploration 1042 and data analysis 1043 operations, described ingreater detail herein. In certain embodiments, the data discoveryoperations may be performed by accessing various multi-structured, bigdata 304 sources. In certain embodiments, as described in greater detailherein, the multi-structured big data 304 sources may include publicdata 412, proprietary data 414, transaction data 416, social data 418,device data 422, ambient data 424, or a combination thereof.

In various embodiments, once the data discovery operations have beencompleted, certain data engineering operations may be performed in step922 to prepare the sourced data for use in the cognitive agentdevelopment 926 phase. In various embodiments, the data engineeringoperations may be performed on certain of the multi-structured, big data304 sources. In certain embodiments, the data engineering operations mayinclude traditional 1044 extract, transform, load (ETL) operations. Incertain embodiments, the data engineering may include cognitiveagent-assisted ETL 1045 operations. In certain embodiments, the dataengineering operations may include data pipeline configuration 146operations to skilled practitioners of the art.

In certain embodiments, once the data sourcing 918 phase has beencompleted, the cognitive agent development 926 phase may be initiated instep 928 with development of one or more machine learning (ML) modelsassociated with the cognitive process. In various embodiments,operations associated with the ML model development may includeexploratory data analysis 1047, data quality and viability assessment1048, and feature identification based upon certain datacharacteristics, or a combination thereof. In certain embodiments,operations associated with the ML model development may include featureprocessing 1050, algorithm evaluation 1051 and assessment 1052,development of new algorithms 1053, and model training 1054, or acombination thereof.

In certain embodiments, any cognitive skills associated with thecognitive process that may not currently exist may then be developed instep 930. In certain embodiments, an ML model developed in step 928 maybe used to develop a cognitive skill in step 930. In certainembodiments, operations associated with the development of a cognitiveskill may include determining the value of a particular cognitive skill1055, implementing one or more actions 1056 associated with a cognitiveskill, and deploying a cognitive skill's action 1057, or a combinationthereof. In various embodiments, operations associated with thedevelopment of a cognitive skill may include the preparation of certaintest data 1058.

In certain embodiments, operations associated with the development of acognitive skill may include defining and deploying a particularcognitive skill's metadata 1059. In certain embodiments, operationsassociated with the development of a cognitive skill may includepreparing a particular cognitive skill as a cognitive process component1060, described in greater detail herein. In certain embodiments,operations associated with the development of a cognitive skill mayinclude unit testing and debugging 1061 one or more actions associatedwith a particular cognitive skill. In certain embodiments, operationsassociated with acquiring cognitive process components may then beperformed in step 932. In certain embodiments, the operations mayinclude identifying 1062 and acquiring 1063 one or more cognitiveprocess components.

In certain embodiments, operations associated with composing a cognitiveagent may then be performed in step 934. In certain embodiments,cognitive process components acquired in step 932 may be used to composethe cognitive agent. In certain embodiments, the operations associatedwith composing a cognitive agent may include searching a repository ofcognitive process components for cognitive skills 1064 or datasets 1065associated with the cognitive process.

In certain embodiments, the operations associated with composing acognitive agent may include decomposing a cognitive agent intoassociated cognitive skills 1066. In certain embodiments, the operationsassociated with composing a cognitive agent may include composing acognitive agent 1067, establishing connections to associated cognitiveskills and data sets 1068, and deploying the cognitive agent 1069, or acombination thereof In certain embodiments, the foregoing steps in thecognitive agent development 926 phase may then be iteratively repeateduntil all needed cognitive agents have been developed.

In certain embodiments, once the cognitive agent development 926 phasehas been completed, quality assurance and user acceptance operationsassociated with the cognitive process are respectively performed in step936 and 938. In certain embodiments, the quality assurance operationsmay include establishing test plans 1070 for the cognitive process. Incertain embodiments, the quality assurance operations may includeverifying the cognitive process meets specified requirements 1071associated with the cognitive process.

In certain embodiments, the quality assurance operations may includevalidating the cognitive process fulfill its intended purpose 1072. Incertain embodiments, the quality assurance operations may includeassessing the cognitive process' rate of learning 1073. In certainembodiments, the use acceptance operations may include validating thecognitive process fulfills its intended purpose 1074. In certainembodiments, the user acceptance operations may include assessing thecognitive process in the context of the user's organization 1073.

In certain embodiments, the cognitive process is then promoted in step940, as described in greater detail herein, into a production phase. Incertain embodiments, operations associated with the production phase mayinclude deploying one or more cognitive agents into production 1076. Incertain embodiments, operations associated with the production phase mayinclude capturing and reprocessing data generated by the system 1077. Incertain embodiments, operations associated with the production phase mayinclude monitoring the system's technical performance 1078.

In certain embodiments, once the cognitive process is operating in theproduction phase, ongoing system monitoring operations are performed instep 942 to collect certain performance data. In certain embodiments,the system monitoring operations may include updating a continuousintegration process 1079. In certain embodiments, the system monitoringoperations may include updating infrastructure monitoring processes1080.

In certain embodiments, the performance data resulting from themonitoring operations performed in step 942 may them be used in step 944to perform various Key Performance Indicator (KPI) evaluationoperations. In certain embodiments, the KPI evaluation operations mayinclude monitoring 1081 and analyzing 1082 the system's businessperformance. In certain embodiments, the KPI evaluation operations mayinclude making recommendations to improve 1083 the systems businessperformance.

In certain embodiments, the results of the KPI evaluations may be usedas feedback to improve the performance of the cognitive process in theproduction 940 phase. In certain embodiments, the results of the KPIevaluations may be provided as input in step 904 to determine additionaloperational and performance parameters related to the cognitive process.In certain embodiments, these additional operational and performanceparameters may be used to repeat one or more steps associated with thelifecycle of the cognitive process to revise its functionality, improveits performance, or both.

FIGS. 11a and 11b are a simplified process flow showing the lifecycle ofaugmented intelligence agents implemented in accordance with anembodiment of the invention to perform augmented intelligence system(AIS) operations. In certain embodiments, a cognitive agent lifecyclemay include a cognitive agent composition 1102 phase and a cognitiveagent confirmation 1104 phase. In certain embodiments, the cognitiveagent composition 1102 phase may be initiated with the definition of acognitive process use case in step 1106, followed by architecting anassociated solution in step 1108.

One or more cognitive skills associated with the architected solutionare then defined, developed and tested in step 1110. In certainembodiments, a machine learning (ML) model associated with thearchitected solution is defined in step 1112. In certain embodiment,cognitive actions, described in greater detail herein, are defined forthe ML model in step 1114. In certain embodiments, data sources for thearchitected solution are identified in step 1116 and correspondingdatasets are defined in step 1118.

The ML model definitions defined in step 1112 are then used in step 1120to define variables that need to be secured in the implementation ofeach associated AIS region, described in greater detail herein.Likewise, the data sources identified in step 1116 are used in step 1122to define data sources corresponding to each associated AIS region.Thereafter, the data sources defined in step 1122 and the datasetsdefined in step 1118 are used in step 1124 to define datasets that willbe used to compose a cognitive agent in step 1128. Once the datasetshave been developed in in step 1124, they are used to curate and uploadtraining data to associated data source connections in step 1126.

Cognitive agent compositions operations are then initiated in step 1128by creating a cognitive agent instance in step 1130. Once created, thesecured variables defined in step 1120 are added to one or morecognitive skills, which in turn are configured in step 1132. The MLmodel actions defined in step 914 are then used in step 1134 to defineinput and output services for the one or more cognitive skillsconfigured in step 1132. Thereafter, the datasets developed in step 1124are used in step 1136, along with the training data curated and uploadedin step 1126 to define dataset connections. A dataflow is then createdfor the cognitive agent in step 1138 and mapped in step 1140.

The cognitive agent confirmation 1104 phase is then initiated in step1142 by testing various service associated with the cognitive agentcomposed in step 1128. Thereafter, a cognitive agent snapshot 1144,described in greater detail herein, is then created in step 1144. Incertain embodiments, the cognitive agent snapshot 1144 may includeversioning and other descriptive information associated with thecognitive agent.

An instance of the cognitive agent is then initiated in step 1146. Incertain embodiments, initiation of the cognitive agent may includepromoting a snapshot of the cognitive agent in step 1148 and enablingstart and stop triggers in step 1150. The instance of the cognitiveagent that was initiated in step 1146 is then invoked for execution instep 1152, followed by performing queries and filtering associatedresponses in step 1154. In certain embodiments, log entriescorresponding to the operations performed in step 1142 through 1154 arereviewed in step 1156.

FIG. 12 is a simplified block diagram of an augmented intelligencesystem (AIS) implemented in accordance with an embodiment of theinvention to perform pattern-based continuous learning operations. Incertain embodiments, the pattern-based continuous learning operations1202 may include a data ingestion and processing 1204 phase, followed bythe performance of certain cognitive processes, described in greaterdetail, during a cognitive insights 1206 phase. In certain embodiments,the cognitive insight 1206 phase is followed by a cognitive action 1208phase, which in turn is followed by a cognitive learning 1210 phase. Incertain embodiments, the process is continued, proceeding with the dataingestion and processing 1204 phase.

In certain embodiments, multi-structured big data sources 304 may bedynamically ingested during the data ingestion and processing 1204phase. In certain embodiments, based upon a particular context,extraction, parsing, and tagging operations are performed on language,text and images they contain to generate associated datasets 1214. Incertain embodiments, the resulting datasets may include varioustransaction histories 1216, customer relationship management (CRM) feeds1218, market data feeds 1220, news feeds 1222, social media feeds 1224,and so forth.

In certain embodiments automated feature extraction and modelingoperations may be performed on the datasets 1214 to generate one or morecognitive models 222, described in greater detail herein. In certainembodiments, the cognitive models 222 may include quantitative 1228models, qualitative 1230 models, ranking 1232 models, news topic 1234models, sentiment 1236 models, and so forth, In various embodiments, theresulting cognitive models may be implemented to map certain datasets1214 to a cognitive graph 260, described in greater detail herein.

In various embodiments, the cognitive models 222 and the datasets 1214mapped to the cognitive graph 260 may be used in the composition ofcertain cognitive skills 226, as likewise described in greater detailherein. In certain embodiments, the cognitive skills 226 may include aportfolio profile builder 1242 skill, a client profile builder 1244skill, a market data pipeline 1246 skill, a market event detection 1248skill, a cognitive insights ranking 1250 skill, and so forth. Aslikewise described in greater detail herein, the resulting cognitiveskills 226 may then be used in various embodiments to generate certaincognitive agents 250. In certain embodiments, the resulting cognitiveagents 250 may include sourcing 432 agents, destination 434 agents,engagement 436 agents, compliance 438 agents, and so forth.

In certain embodiments, the sourcing 432 agent may be implemented tosource various multi-structured big data 304 sources, described ingreater detail herein. In certain embodiments, the sourcing 432 agentmay include a batch upload agent, an Application Program Interface (API)connectors agent, a real-time streams agent, a Structured Query Language(SQL)/Not Only SQL (NoSQL) databases agent, a message engines agent, atransaction sourcing agent, or some combination thereof. Skilledpractitioners of the art will recognize that many such examples ofsourcing 432 agents are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In certain embodiments, the resulting cognitive agents 250 may beimplemented during the cognitive insights 1206 phase, as described ingreater detail herein, to perform various cognitive processes 326. Invarious embodiments, the performance of certain cognitive processes 326may result in the generation of one or more cognitive insights. Invarious embodiments, the cognitive insights may include the provision ofcertain actionable recommendations and notifications to a user duringthe cognitive action 1208 phase. In various embodiments, certainfeatures from newly-observed data may be automatically extracted fromuser feedback during the learning 1010 phase to improve variouscognitive models 222.

As an example, a first query from a user may be submitted to the AIS 118system during the cognitive insights 1206 phase, which results in thegeneration of a first cognitive insight, which is then provided to theuser. In response, the user may respond by providing a first response,or perhaps a second query, either of which is provided in the samecontext as the first query. The AIS 118 receives the first response orsecond query, performs various AIS 118 operations, and provides the usera second cognitive insight. As before, the user may respond with asecond response or a third query, again in the context of the firstquery. Once again, the AIS 118 performs various AIS 118 operations andprovides the user a third cognitive insight, and so forth. In thisexample, the provision of cognitive insights to the user, and theirvarious associated responses, results in a stateful dialog that evolvesover time.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A computer-implementable method for cognitiveinformation processing, comprising: receiving data from a plurality ofdata sources; processing the data from the plurality of data sources toprovide cognitively processed insights via an augmented intelligencesystem, the augmented intelligence system executing on a hardwareprocessor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function; performing a learning operation toiteratively improve the cognitively processed insights over time; and,providing the cognitively processed insights to a destination, thedestination comprising a cognitive application, the cognitiveapplication enabling a user to interact with the cognitive insights. 2.The method of claim 1, wherein: the augmented intelligence systemcomprises a cognitive process foundation.
 3. The method of claim 2,wherein: the cognitive process foundation performs at least one of aplurality of cognitive computing functions, the plurality of cognitivecomputing functions comprising simplification of data and computeresource access, sharing and control operations, composition andorchestration of artificial intelligence systems and governance andcontrol operations.
 4. The method of claim 1, wherein: the augmentedintelligence system comprises at least one of a plurality of cognitiveprocesses.
 5. The method of claim 4, wherein: the plurality of cognitiveprocesses comprise an intelligent user engagement process, an augmentedprocess engagement process and a cognitive application process.
 6. Themethod of claim 1, wherein: the augmented intelligence system comprisesa cognitive infrastructure.
 7. A system comprising: a hardwareprocessor; a data bus coupled to the hardware processor; and anon-transitory, computer-readable storage medium embodying computerprogram code, the non-transitory, computer-readable storage medium beingcoupled to the data bus, the computer program code interacting with aplurality of computer operations and comprising instructions executableby the hardware processor and configured for: receiving data from aplurality of data sources; processing the data from the plurality ofdata sources to provide cognitively processed insights via an augmentedintelligence system, the augmented intelligence system executing on ahardware processor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function; performing a learning operation toiteratively improve the cognitively processed insights over time; and,providing the cognitively processed insights to a destination, thedestination comprising a cognitive application, the cognitiveapplication enabling a user to interact with the cognitive insights. 8.The system of claim 7, wherein: the augmented intelligence systemcomprises a cognitive process foundation.
 9. The system of claim 8,wherein: the cognitive process foundation performs at least one of aplurality of cognitive computing functions, the plurality of cognitivecomputing functions comprising simplification of data and computeresource access, sharing and control operations, composition andorchestration of artificial intelligence systems and governance andcontrol operations.
 10. The system of claim 7, wherein: the augmentedintelligence system comprises at least one of a plurality of cognitiveprocesses.
 11. The system of claim 10, wherein: the plurality ofcognitive processes comprise an intelligent user engagement process, anaugmented process engagement process and a cognitive applicationprocess.
 12. The system of claim 7, wherein: the augmented intelligencesystem comprises a cognitive infrastructure.
 13. A non-transitory,computer-readable storage medium embodying computer program code, thecomputer program code comprising computer executable instructionsconfigured for: receiving data from a plurality of data sources;processing the data from the plurality of data sources to providecognitively processed insights via an augmented intelligence system, theaugmented intelligence system executing on a hardware processor of aninformation processing system, the augmented intelligence system and theinformation processing system providing a cognitive computing function;performing a learning operation to iteratively improve the cognitivelyprocessed insights over time; and, providing the cognitively processedinsights to a destination, the destination comprising a cognitiveapplication, the cognitive application enabling a user to interact withthe cognitive insights.
 14. The non-transitory, computer-readablestorage medium of claim 13, wherein: the augmented intelligence systemcomprises a cognitive process foundation.
 15. The non-transitory,computer-readable storage medium of claim 14, wherein: the cognitiveprocess foundation performs at least one of a plurality of cognitivecomputing functions, the plurality of cognitive computing functionscomprising simplification of data and compute resource access, sharingand control operations, composition and orchestration of artificialintelligence systems and governance and control operations.
 16. Thenon-transitory, computer-readable storage medium of claim 13, wherein:the augmented intelligence system comprises at least one of a pluralityof cognitive processes.
 17. The non-transitory, computer-readablestorage medium of claim 16, wherein: the plurality of cognitiveprocesses comprise an intelligent user engagement process, an augmentedprocess engagement process and a cognitive application process.
 18. Thenon-transitory, computer-readable storage medium of claim 13, wherein:the augmented intelligence system comprises a cognitive infrastructure.19. The non-transitory, computer-readable storage medium of claim 13,wherein: the computer executable instructions are deployable to a clientsystem from a server system at a remote location.
 20. Thenon-transitory, computer-readable storage medium of claim 13, wherein:the computer executable instructions are provided by a service providerto a user on an on-demand basis.